10.57647/ijm2c.2026.1604.23

Designing a Structural Model for Implementing Maintenance 4.0 in Iran’s Oil and Gas Industry with a Sustainability-Oriented Approach

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  • Seyed Vali-Allah Hosseini Falehi1,
  • Mohammad Jalali Varnamkhasti*2,
  • Mohammad Mashhadizadeh3,
  • Mansoureh Pourmiri4,
  1. Department of Industrial Management, Isf.C., Islamic Azad University, Isfahan, Iran
  2. Department of Mathematics, Isf.C., Islamic Azad University, Isfahan, Iran
  3. Department of Management, Institute of Market and Business, Isf.C., Islamic Azad University, Isfahan, Iran
  4. Department of Communication Sciences, Institute of Market and business, Isf.C., Islamic Azad University, Isfahan, Iran

Received: 05-09-2025

Revised: 03-02-2026

Accepted: 12-02-2026

Published Online: 23-02-2026

Copyright (c) 2025 Seyed Vali-Allah Hosseini Falehi, Mohammad Jalali Varnamkhasti, Mohammad Mashhadizadeh, Mansoureh Pourmiri (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Hosseini Falehi, S. V.-A., Jalali Varnamkhasti, M., Mashhadizadeh, M., & Pourmiri, M. (2025). Designing a Structural Model for Implementing Maintenance 4.0 in Iran’s Oil and Gas Industry with a Sustainability-Oriented Approach. International Journal of Mathematical Modelling & Computations. https://doi.org/10.57647/ijm2c.2026.1604.23
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Abstract

This study was conducted with the aim of presenting a structural model for implementing Maintenance 4.0 technologies with an emphasis on sustainability in Iran's oil and gas industry. To analyze the relationships between variables, machine learning techniques and artificial neural networks were employed. Data were collected through a researcher-designed questionnaire and included variables such as Maintenance 4.0 technologies, organizational digital readiness, implementation challenges, and organizational sustainable performance. The results indicated that the most influential factors on sustainable organizational performance were data mining and the Internet of Things while technical infrastructure and employee skills also had indirect effects. The model's accuracy was confirmed with a coefficient of determination of 0.89, a mean squared error of 0.057, and a mean absolute percentage error of 2.6%. The final model can be used as a decision-support tool in formulating smart and sustainable maintenance policies in Iran's energy sector.

Keywords

  • Maintenance 4.0,
  • Sustainability,
  • Machine Learning,
  • Artificial Neural Network,
  • Oil and Gas Industry
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References

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